Research on Intelligent Recognition Solution of Tobacco Disease on Android Platform
In order to improve the recognition accuracy of tobacco diseases, improve the recognition efficiency and convenience, and reduce the recognition cost, this project carried out the research on the recognition technology of tobacco diseases based on deep learning. First, the data set was established....
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Published in | 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) pp. 1 - 4 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
16.12.2022
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Subjects | |
Online Access | Get full text |
DOI | 10.1109/ICARCE55724.2022.10046516 |
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Abstract | In order to improve the recognition accuracy of tobacco diseases, improve the recognition efficiency and convenience, and reduce the recognition cost, this project carried out the research on the recognition technology of tobacco diseases based on deep learning. First, the data set was established. The data set is consisted of several kinds of common tobacco diseases images which were labeled according to the experts' diagnosis results. Second, the YOLOv7 network model was studied and pruned considering the recognition rate and accurate. Third, the pruned model was trained using the established training dataset. Then, the trained model is ported to Android system. Finally, an experimental testing was carried out, and the results show that the model can run efficiently in Android system with the detection accuracy above 90%. |
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AbstractList | In order to improve the recognition accuracy of tobacco diseases, improve the recognition efficiency and convenience, and reduce the recognition cost, this project carried out the research on the recognition technology of tobacco diseases based on deep learning. First, the data set was established. The data set is consisted of several kinds of common tobacco diseases images which were labeled according to the experts' diagnosis results. Second, the YOLOv7 network model was studied and pruned considering the recognition rate and accurate. Third, the pruned model was trained using the established training dataset. Then, the trained model is ported to Android system. Finally, an experimental testing was carried out, and the results show that the model can run efficiently in Android system with the detection accuracy above 90%. |
Author | Yu, Feiyong Li, Yapeng Sun, Shaohua Li, Jingjing Xu, Yi Qi, Kepei |
Author_xml | – sequence: 1 givenname: Jingjing surname: Li fullname: Li, Jingjing organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China – sequence: 2 givenname: Yi surname: Xu fullname: Xu, Yi organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China – sequence: 3 givenname: Yapeng surname: Li fullname: Li, Yapeng organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China – sequence: 4 givenname: Kepei surname: Qi fullname: Qi, Kepei organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China – sequence: 5 givenname: Feiyong surname: Yu fullname: Yu, Feiyong organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China – sequence: 6 givenname: Shaohua surname: Sun fullname: Sun, Shaohua email: 18137150576@163.com organization: Henan Tobacco Company Jiyuan Company,Jiyuan,China |
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Snippet | In order to improve the recognition accuracy of tobacco diseases, improve the recognition efficiency and convenience, and reduce the recognition cost, this... |
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SubjectTerms | Android deep learning intelligence recognition Tobacco disease Yolo V7 |
Title | Research on Intelligent Recognition Solution of Tobacco Disease on Android Platform |
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